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Query-driven Active Surveying for Collective Classification
TLDR
This work develops an algorithm which adaptively selects survey nodes by estimating which form of smoothness is most appropriate, and evaluates its algorithm on several network datasets and demonstrates its improvements over standard active learning methods.
A short introduction to probabilistic soft logic
TLDR
This paper provides an overview of the PSL language and its techniques for inference and weight learning.
Beyond Parity: Fairness Objectives for Collaborative Filtering
TLDR
This work identifies the insufficiency of existing fairness metrics and proposes four new metrics that address different forms of unfairness that can be optimized by adding fairness terms to the learning objective.
Joint Models of Disagreement and Stance in Online Debate
TLDR
This work comprehensively evaluates the possible modeling choices on eight topics across two online debate corpora and introduces a scalable unified probabilistic modeling framework for stance classification models that are collective, reason about disagreement, and can model stance at either the author level or at the post level.
'Beating the news' with EMBERS: forecasting civil unrest using open source indicators
We describe the design, implementation, and evaluation of EMBERS, an automated, 24x7 continuous system for forecasting civil unrest across 10 countries of Latin America using open source indicators
Modeling Learner Engagement in MOOCs using Probabilistic Soft Logic
TLDR
This paper uses probabilistic soft logic (PSL) to model student engagement by capturing domain knowledge about student interactions and performance and demonstrates that modeling engagement is helpful in predicting student performance.
Learning Latent Engagement Patterns of Students in Online Courses
TLDR
A framework for modeling and understanding student engagement in online courses based on student behavioral cues is developed and it is demonstrated that the latent formulation for engagement helps in predicting student survival across three MOOCs.
Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction
TLDR
H hinge-loss Markov random fields (HL-MRFs), an expressive class of graphical models with log-concave density functions over continuous variables, which can represent confidences in discrete predictions, are used.
Semantic Model Vectors for Complex Video Event Recognition
TLDR
This study reveals that the proposed semantic model vectors representation outperforms-and is complementary to-other low-level visual descriptors for video event modeling, and validates it not only as the best individual descriptor, outperforming state-of-the-art global and local static features as well as spatio-temporal HOG and HOF descriptors, but also as the most compact.
Learning a Distance Metric from a Network
TLDR
A method for optimizing SPML based on stochastic gradient descent which removes the running-time dependency on the size of the network and allows the method to easily scale to networks of thousands of nodes and millions of edges.
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